SAR (synthetic aperture radar) image change detection method based on high-order neighborhood TMF (triplet Markov random field) model

Abstract

The invention discloses an SAR (synthetic aperture radar) image change detection method based on a high-order neighborhood triplet Markov random field model, and mainly solves problems of high false detection amount and low overall precision of an existing method. The SAR image change detection method comprises the following steps: 1, inputting two time phase SAR images and generating a differential image; 2, initializing a labeling field X; 3, initializing a likelihood parameter; 4, performing definition on the initialized labeling field X by adopting a 3*3 neighborhood and initializing an auxiliary field U; 5, constructing a priori potential energy function comprising a homogeneous region, a heterogeneous region and a U field part by adopting a 5*5 high-order neighborhood; 6, updating the labeling field X and the auxiliary field U; 7, updating the likelihood parameter according to the updated labeling field X; 8, performing iterative updating on the labeling field X and the auxiliary field U and obtaining a final change detection result. Compared with the prior art, the SAR image change detection method reduces the false detection amount, improves the overall detection precision, enhances the noise robustness, and can be used for SAR image recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to image change detection, and can be used for monitoring and evaluating ground object state changes on SAR images. Background technique [0002] Synthetic aperture radar SAR image change detection refers to detecting the change information of the ground objects in the area by analyzing two SAR images of the same area at different times. Due to the all-weather and all-time characteristics of synthetic aperture radar SAR, SAR image change detection technology has more and more applications in fields such as agricultural and forestry exploration, environmental monitoring, disaster assessment, and resource utilization, so it has high detection accuracy SAR image change detection method become the focus of research. [0003] One of the more classic methods of SAR image change detection is based on statistical models, such as the Markov random field MRF model and the triple Markov ra...

AI Technical Summary

Problems solved by technology

However, the study found that the auxiliary field U in this paper cannot accurately reflect the non-stationary characteristics of SAR images, and cannot distinguish homogeneous regions from non-homogeneous regions.

In addition, due to the existence of speckle noise in SAR images, when faced with SAR images with high noise intensity, the four-neighborhood system cannot suppress the influence of noise very well, and there are still high numbers of false detections in the obtained results.

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